Statistical shape modeling using MDL incorporating shape, appearance, and expert knowledge

Authors:
Aaron D. Ward;Ghassan Hamarneh
Affiliations:
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada;Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

We propose a highly automated approach to the point correspondence problem for anatomical shapes in medical images. Manual landmarking is performed on a small subset of the shapes in the study, and a machine learning approach is used to elucidate the characteristic shape and appearance features at each landmark. A classifier trained using these features defines a cost function that drives key landmarks to anatomically meaningful locations after MDL-based correspondence establishment. Results are shown for artificial examples as well as real data.